Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex

Journal name:
Nature Neuroscience
Volume:
19,
Pages:
40–47
Year published:
DOI:
doi:10.1038/nn.4181
Received
Accepted
Published online

Abstract

DNA methylation (DNAm) is important in brain development and is potentially important in schizophrenia. We characterized DNAm in prefrontal cortex from 335 non-psychiatric controls across the lifespan and 191 patients with schizophrenia and identified widespread changes in the transition from prenatal to postnatal life. These DNAm changes manifest in the transcriptome, correlate strongly with a shifting cellular landscape and overlap regions of genetic risk for schizophrenia. A quarter of published genome-wide association studies (GWAS)-suggestive loci (4,208 of 15,930, P < 10−100) manifest as significant methylation quantitative trait loci (meQTLs), including 59.6% of GWAS-positive schizophrenia loci. We identified 2,104 CpGs that differ between schizophrenia patients and controls that were enriched for genes related to development and neurodifferentiation. The schizophrenia-associated CpGs strongly correlate with changes related to the prenatal-postnatal transition and show slight enrichment for GWAS risk loci while not corresponding to CpGs differentiating adolescence from later adult life. These data implicate an epigenetic component to the developmental origins of this disorder.

At a glance

Figures

  1. Differentially methylated loci comparing pre- and postnatal control subjects show large differences in DNA methylation.
    Figure 1: Differentially methylated loci comparing pre- and postnatal control subjects show large differences in DNA methylation.

    (a) Distribution of differences in DNAm across all individual CpGs and probes revealed many sites with large changes in DNAm. Insets, examples of differentially methylated loci. For box plots, center line is the median, limits are the interquartile range (IQR), and whiskers are 1.5× the IQR. (b) An example DMR representing regional differences in DNAm levels. (c) An example methylation block representing long-range changes. Proportion methylation is shown on the y axis of the insets in ac. Gene annotation panels in b and c are based on Ensembl annotation; dark blue represents exons and light blue represents introns.

  2. A changing neuronal phenotype across brain development.
    Figure 2: A changing neuronal phenotype across brain development.

    (ad) Composition proportions per sample plotted versus age; the first subpanel in each represents age in post-conception weeks, and the remaining three subpanels show age in years. (e) Proportion of variance, R2, explained by cell composition at each CpG (gray) in which the proportion of CpGs showing significant age stage-related (fetal versus postnatal) changes are shown in red. (f) The estimated proportion of embryonic stem cells (ESCs) versus post-conception days from a previous study23 showed strong association. NPCs, neural progenitor cells; PCW, post-conception weeks.

  3. Examples of meQTLs for six GWAS-associated variants with nearby DNA methylation levels.
    Figure 3: Examples of meQTLs for six GWAS-associated variants with nearby DNA methylation levels.

    y axis shows DNA methylation level at a particular probe and the x axis represents genotype at a particular SNP. P value corresponds to the effect of genotype on DNAm level, adjusting for ancestry and epigenetic principal components. For box plots, center line is the median, limits are the IQR, and whiskers are 1.5× the IQR.

  4. Examples of meQTLs for 12 GWAS-positive loci for schizophrenia.
    Figure 4: Examples of meQTLs for 12 GWAS-positive loci for schizophrenia.

    y axis shows DNA methylation level at a particular probe and x axis represents genotype at a particular SNP. P value corresponds to the effect of genotype on DNAm level, adjusting for ancestry and epigenetic principal components. For box plots, center line is the median, limits are the IQR, and whiskers are 1.5× the IQR.

  5. Principal component analysis (PCA) demonstrates genome-wide changes in DNAm comparing pre- and post-natal samples.
    Supplementary Fig. 1: Principal component analysis (PCA) demonstrates genome-wide changes in DNAm comparing pre- and post-natal samples.

    The first principal component (PC, explaining 55.6% of the variance in the data) cleanly separately pre- and post-natal samples, with samples from children (ages 0-13) demonstrating directional consistency across aging.

  6. Example differentially methylated regions (DMRs) for pre- versus post-natal differences in DNA methylation, as Figure 1B.
    Supplementary Fig. 2: Example differentially methylated regions (DMRs) for pre- versus post-natal differences in DNA methylation, as Figure 1B.

    Proportion methylation is shown on the y-axis of each respective top-most panel. Gene annotation panels in (B) and (C) are based on Ensembl annotation – dark blue represents exons and light blue represents introns.

  7. Directionally balanced associations between DNAm and nearby gene expression levels at individual CpGs.
    Supplementary Fig. 3: Directionally balanced associations between DNAm and nearby gene expression levels at individual CpGs.

    Pearson correlations between DNAm levels at individual CpGs/probes and nearby genes, which contains approximately equal numbers of positive and negative correlations.

  8. Directionally balanced associations between DNAm and nearby gene expression levels at DMRs.
    Supplementary Fig. 4: Directionally balanced associations between DNAm and nearby gene expression levels at DMRs.

    Pearson correlations between DNAm levels at DMRs to nearby genes, stratified by the location of the DMR relative to that gene. These correlations are relatively balanced for positive and negative correlations overall, and by many classes of annotation.

  9. Proportion of variance explained in gene expression levels by cell composition estimated from DNAm data.
    Supplementary Fig. 5: Proportion of variance explained in gene expression levels by cell composition estimated from DNAm data.
  10. Composition change across fetal brain development.
    Supplementary Fig. 6: Composition change across fetal brain development.

    Shown are the composition estimates versus post-conception data for the 4 additional cell types, with Pearson correlation printed in the top right corner, as Figure 2F

  11. Neuronal composition differences by brain region and age in the BrainSpan dataset.
    Supplementary Fig. 7: Neuronal composition differences by brain region and age in the BrainSpan dataset.

    Each point is a sample, colored by age, and stratified by brain regions. The vertical dashed line separates the cortical brain regions from non-cortical regions.

  12. Effect of adult meQTLs in fetal samples.
    Supplementary Fig. 8: Effect of adult meQTLs in fetal samples.
  13. Cellular composition profiles by diagnosis.
    Supplementary Fig. 9: Cellular composition profiles by diagnosis.

    (A) Distributions of NeuN- estimates per sample by processing plate and diagnosis; all p-values for diagnosis within a plate were > 0.01. (B) NeuN- proportion explains the first principal component of autosomal DNAm levels in adult samples.

  14. Negative control principal components associate with processing plate and slide.
    Supplementary Fig. 10: Negative control principal components associate with processing plate and slide.
  15. Samples on one processing plate ([ldquo]Plate2[rdquo]) show magnified differential methylation effects for schizophrenia.
    Supplementary Fig. 11: Samples on one processing plate (“Plate2”) show magnified differential methylation effects for schizophrenia.

    While the T-statistics within each plate are correlated, those calculated only within samples on Plate2 were almost an order of magnitude larger than similar statistics calculated only within samples on Plate3. Red: identity line.

  16. Sensitivity analyses of differentially methylated CpGs for schizophrenia.
    Supplementary Fig. 12: Sensitivity analyses of differentially methylated CpGs for schizophrenia.

    We compared the effects sizes of schizophrenia differences from the original statistical model (adjusting for age, sex, race, and 4 negative control PCs) to also including (A) composition estimates from all 5 cell types and (B) smoking status determined by toxicology and (C) antipsychotics by self-report in the final model.

  17. Effect of adult control meQTLs in adult SZ samples.
    Supplementary Fig. 13: Effect of adult control meQTLs in adult SZ samples.
  18. Concordance between schizophrenia and developmental effects across 2,104 CpGs associated with schizophrenia.
    Supplementary Fig. 14: Concordance between schizophrenia and developmental effects across 2,104 CpGs associated with schizophrenia.

    X-axis: change in the DNA methylation (DNAm) levels comparing prenatal versus postnatal samples, Y-axis: change in DNAm level comparing patients with schizophrenia to adult controls. Each point is one CpG probe.

Accession codes

Primary accessions

Gene Expression Omnibus

Referenced accessions

Gene Expression Omnibus

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Author information

  1. These authors contributed equally to this work.

    • Daniel R Weinberger &
    • Joel E Kleinman

Affiliations

  1. Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA.

    • Andrew E Jaffe,
    • Yuan Gao,
    • Amy Deep-Soboslay,
    • Ran Tao,
    • Thomas M Hyde,
    • Daniel R Weinberger &
    • Joel E Kleinman
  2. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

    • Andrew E Jaffe
  3. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

    • Andrew E Jaffe
  4. Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.

    • Thomas M Hyde &
    • Daniel R Weinberger
  5. Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.

    • Thomas M Hyde &
    • Daniel R Weinberger
  6. Department of Neuroscience and the Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.

    • Daniel R Weinberger

Contributions

A.E.J. designed the study, performed the data analysis and oversaw the writing of the manuscript. Y.G. oversaw the data generation. A.D.-S. collected phenotype data on all subjects. R.T. performed DNA extractions and contributed to the data generation. T.M.H. collected brain samples and performed tissue dissections to obtain biological materials. D.R.W. designed the study, contributed to the data analysis and interpretation of the results, and oversaw the writing of the manuscript. J.E.K. collected brain samples and provided clinical interpretation of the results. All authors contributed to the writing of the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

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Author details

Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: Principal component analysis (PCA) demonstrates genome-wide changes in DNAm comparing pre- and post-natal samples. (75 KB)

    The first principal component (PC, explaining 55.6% of the variance in the data) cleanly separately pre- and post-natal samples, with samples from children (ages 0-13) demonstrating directional consistency across aging.

  2. Supplementary Figure 2: Example differentially methylated regions (DMRs) for pre- versus post-natal differences in DNA methylation, as Figure 1B. (145 KB)

    Proportion methylation is shown on the y-axis of each respective top-most panel. Gene annotation panels in (B) and (C) are based on Ensembl annotation – dark blue represents exons and light blue represents introns.

  3. Supplementary Figure 3: Directionally balanced associations between DNAm and nearby gene expression levels at individual CpGs. (74 KB)

    Pearson correlations between DNAm levels at individual CpGs/probes and nearby genes, which contains approximately equal numbers of positive and negative correlations.

  4. Supplementary Figure 4: Directionally balanced associations between DNAm and nearby gene expression levels at DMRs. (137 KB)

    Pearson correlations between DNAm levels at DMRs to nearby genes, stratified by the location of the DMR relative to that gene. These correlations are relatively balanced for positive and negative correlations overall, and by many classes of annotation.

  5. Supplementary Figure 5: Proportion of variance explained in gene expression levels by cell composition estimated from DNAm data. (56 KB)
  6. Supplementary Figure 6: Composition change across fetal brain development. (90 KB)

    Shown are the composition estimates versus post-conception data for the 4 additional cell types, with Pearson correlation printed in the top right corner, as Figure 2F

  7. Supplementary Figure 7: Neuronal composition differences by brain region and age in the BrainSpan dataset. (137 KB)

    Each point is a sample, colored by age, and stratified by brain regions. The vertical dashed line separates the cortical brain regions from non-cortical regions.

  8. Supplementary Figure 8: Effect of adult meQTLs in fetal samples. (127 KB)
  9. Supplementary Figure 9: Cellular composition profiles by diagnosis. (47 KB)

    (A) Distributions of NeuN- estimates per sample by processing plate and diagnosis; all p-values for diagnosis within a plate were > 0.01. (B) NeuN- proportion explains the first principal component of autosomal DNAm levels in adult samples.

  10. Supplementary Figure 10: Negative control principal components associate with processing plate and slide. (78 KB)
  11. Supplementary Figure 11: Samples on one processing plate (“Plate2”) show magnified differential methylation effects for schizophrenia. (130 KB)

    While the T-statistics within each plate are correlated, those calculated only within samples on Plate2 were almost an order of magnitude larger than similar statistics calculated only within samples on Plate3. Red: identity line.

  12. Supplementary Figure 12: Sensitivity analyses of differentially methylated CpGs for schizophrenia. (43 KB)

    We compared the effects sizes of schizophrenia differences from the original statistical model (adjusting for age, sex, race, and 4 negative control PCs) to also including (A) composition estimates from all 5 cell types and (B) smoking status determined by toxicology and (C) antipsychotics by self-report in the final model.

  13. Supplementary Figure 13: Effect of adult control meQTLs in adult SZ samples. (81 KB)
  14. Supplementary Figure 14: Concordance between schizophrenia and developmental effects across 2,104 CpGs associated with schizophrenia. (58 KB)

    X-axis: change in the DNA methylation (DNAm) levels comparing prenatal versus postnatal samples, Y-axis: change in DNAm level comparing patients with schizophrenia to adult controls. Each point is one CpG probe.

PDF files

  1. Supplementary Text and Figures (6,786 KB)

    Supplementary Figures 1–14 and Supplementary Analysis

  2. Supplementary Methods Checklist (393 KB)

Excel files

  1. Supplementary Table 1 (11 KB)

    Demographic data for the samples analyzed, stratified by age group and diagnosis.
    P-values depict the differences between the demographic data by the cases and adult controls.

  2. Supplementary Table 3 (955 KB)

    Gene ontology (GO) enrichment statistics for those DMRs that increase/“up” or decrease/“down” across the transition from pre to postnatal life

  3. Supplementary Table 6 (13 KB)

    Overlap between DNAm changes and chromatin state data from the Epigenome Roadmap project for adult DLPFC
    Bolded cells indicated >2 fold enrichment or depletion compared to the relevant background CpGs/regions

  4. Supplementary Table 8 (6,320 KB)

    NHGRI GWAS catalog annotated by whether each SNP has an meQTL in the DLPFC dataset.

  5. Supplementary Table 9 (1,500 KB)

    meQTLs within the PGC2 SNPs and their proxies

Other

  1. Supplementary Table 2 (1,938 KB)

    Differentially methylated regions (DMRs) and corresponding annotation comparing prenatal and postnatal samples.

  2. Supplementary Table 4 (112 KB)

    Differentially methylated blocks comparing prenatal and postnatal samples

  3. Supplementary Table 5 (538 KB)

    Gene ontology (GO) analysis on the genes contained with the differentially methylated blocks.

  4. Supplementary Table 7 (4 KB)

    Overlap between PGC2 risk regions and DMRs associated with the transition from prenatal to postnatal life, n=31 regions.
    Rank: PGC Rank, P.value: p-value for the region, Position: range of LD block for region, numDMRs: the number of DMRs within the region.

  5. Supplementary Table 10 (557 KB)

    List of differentially methylated CpGs comparing patients with schizophrenia to adult controls.

  6. Supplementary Table 11 (904 KB)

    Gene ontology (GO) analysis for genes near differentially methylated CpGs for diagnosis.

Additional data